A hybrid machine learning algorithm for studying magnetized nanofluid flow containing gyrotactic microorganisms via a vertically inclined stretching surface

The novelty of the present work is to acquire continuous functions as solutions rather than the discrete ones that traditional numerical methods generally produce and to minimize simulation times and higher computation costs that are the fundamental barriers to employing any numerical method. In thi...

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Vydáno v:International journal for numerical methods in biomedical engineering Ročník 40; číslo 1; s. e3780
Hlavní autoři: Chandra, Priyanka, Das, Raja
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Wiley Subscription Services, Inc 01.01.2024
Témata:
Algorithms
Back propagation networks
Brownian motion
Computer Simulation
Continuity (mathematics)
Density
Error analysis
Finite element method
Fluid flow
Hot Temperature
Hydrodynamics
Learning algorithms
Machine learning
Magnetohydrodynamics
Mathematical models
Microorganisms
Nanofluids
Nanoparticles
Neural networks
Numerical analysis
Numerical methods
Parameters
Porosity
Porous media
Prandtl number
Production methods
Regression analysis
Stretching
Temperature
Thermophoresis
Levenberg-Marquardt technique
vertically inclined stretching surface
nanoparticles
Finite Element Method
porous medium
gyrotactic microorganisms
ISSN:2040-7939, 2040-7947, 2040-7947
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Shrnutí:The novelty of the present work is to acquire continuous functions as solutions rather than the discrete ones that traditional numerical methods generally produce and to minimize simulation times and higher computation costs that are the fundamental barriers to employing any numerical method. In this study, a novel hybrid finite element‐based machine learning algorithm utilizing the Levenberg–Marquardt scheme with backpropagation in a neural network (LMBNN) is presented to analyze the nanofluid flow in the presence of magnetohydrodynamics and gyrotactic microorganisms through a vertically inclined stretching surface in a porous medium. Finite Element Method is used to generate the minimum reference dataset for LMBNN by varying six flow parameters in the form of six scenarios. Surface plots are utilized to understand how these scenarios affect velocity, temperature, concentration of nanoparticles, and density of motile microorganisms. Regression analysis, error histogram analysis, and fitness curves based on mean square error all support the LMBNN's effectiveness and dependability. Results reveal that temperature increases with the rise in Brownian motion and thermophoresis parameter, whereas the reverse trend has been noticed for Prandtl number. The motile microorganism density number decreases with the rise in Prandtl numbers but improves with the porosity parameter.
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ISSN:2040-7939
2040-7947
2040-7947
DOI:10.1002/cnm.3780